Legislative Term Limits and Polarization ∗
Michael Olson1 and Jon Rogowski2
1Ph.D Candidate, Department of Government, Harvard University2Assistant Professor, Department of Government, Harvard University
May 24, 2018
Abstract
How do legislative term limits affect representation? Despite proponents’ arguments that
term limits reduce the level of partisan conflict and improve the quality of representation, these
expectations have been subjected to little empirical scrutiny. We argue that term limits in-
crease party polarization by reducing legislators’ electoral incentives and diminishing the value
of elective office, in turn increasing the role of parties in legislative processes. Using a panel de-
sign and data on roll call voting patterns from 1993 to 2014, we show that term limits produced
systematically higher levels of polarization in state legislative voting patterns by increasing the
ideological gap between Republicans’ and Democrats’ voting records and had greater effects on
polarization in states with more professional legislatures. Contrary to the goals of their pro-
ponents, terms limits appear to have exacerbated the legislative consequences of contemporary
partisanship and have implications for understanding how electoral and career incentives affect
legislative outcomes.
∗Brian Lash was a collaborator on an earlier version of this manuscript and we thank him for his contri-butions to the project. We are grateful to Corban Ryan, Enrique Rodriguez, and Michael Scherr for excellentresearch assistance and Steve Rogers for sharing some of the data used in this project. Rogowski thanks theFaculty of Arts and Sciences at Harvard University and the Department of Political Science at WashingtonUniversity in St. Louis for generous research support.
Contemporary frustrations with gridlock in the United States Congress have renewed
interest in term limits as a potential solution from both sides of the partisan aisle. For
instance, in a debate among candidates for the 2012 Republican presidential nomination,
former Utah governor Jon Huntsman declared that “[w]e need a Washington that works
. . . we have a Congress that can’t even figure out how to balance the budget. They need
term limits.”1 Similarly, former Senator Joseph Lieberman remarked that Congress “might
be healthier and less partisan and less rigid if it turned over more often, and term limits are
one way to do that.”2 Even sitting members of Congress have expressed support for limiting
their own terms in office and have introduced legislation in recent congresses to amend the
Constitution and limit the number of years served by members of the House and Senate.3
And in early 2018, Maryland’s Republican Governor Larry Hogan proposed an eight-year
term limit for that states’ legislators.4
As the quotes above suggest, proponents of term limits argue that limiting the number of
terms legislators can serve would reduce the level of partisan conflict, encourage compromise
and cooperation, and improve the quality of representation. Consistent with these claims,
scholars have argued that term limits put an end to “politics as usual” (Petracca 1991) by
producing “citizen legislators” who are more likely to behave in the public interest (Carey,
Niemi, and Powell 1998; Glazer and Wattenberg 1996; Smart and Sturm 2013), more respon-
sive to public opinion and constituent needs (Caress and Kunioka 2012; Chen and Niou 2005;
Grofman 1996), and less beholden to political parties (Malbin and Benjamin 1992). Other
scholars argue, however, that term limits produce more partisan legislatures by reducing
legislators’ incentives to moderate their behavior, instead choosing to vote with the party
1https://goo.gl/p41J7Y.
2https://goo.gl/SMPnHf.
3https://goo.gl/vgYZbR; https://goo.gl/DajdTM; https://goo.gl/QnZDCq.
4https://goo.gl/tcvP53.
1
over their constituents (Cain and Kousser 2004; Cohen and Spitzer 1992; Masket and Shor
2015; Wright 2007; but see also Titiunik and Feher 2017).
In this paper, we study the effects of term limits on legislative behavior in the U.S. states.
We argue that term limits increase party polarization by reducing legislators’ electoral in-
centives and diminishing the value of elective office, in turn increasing the role of parties in
recruiting and supporting legislative candidates. After more than two decades of experience
with term limits, strikingly little is known about their effects on partisanship and polariza-
tion.5 This omission is surprising given the expansive literature on legislative term limits’
effects on other important outcomes, including fiscal policy (Cummins 2013; Keele, Malho-
tra, and McCubbins 2013), legislative expertise (Kousser 2005), policy complexity (Kousser
2006), descriptive representation (Carroll and Jenkins 2005), electoral competition (Daniel
and Lott 1997; Powell 2000), the incumbency advantage (Rogers 2014), attentiveness to dis-
trict interests (Carey, Niemi, and Powell 1998; Carey et al. 2006), cosponsorship activity
(Swift and VanderMolen 2016), abstention rates (Clark and Williams 2014), the distribu-
tion of power within the legislature (Alvarez and Sinclair 2012), and the balance of power
across chambers (Cain and Levin 1999) and branches of government (Baker and Hedge 2013;
Grofman and Sutherland 1996; Miller, Nicholson-Crotty, and Nicholson-Crotty 2011).6
We report new evidence about the relationship between term limits and party polarization
in state legislatures using a panel design and data on roll call voting patterns from 1993 to
2014. Consistent with our argument, we show that term limits produced systematically
higher levels of polarization in state legislative voting patterns by increasing the ideological
gap between Republicans’ and Democrats’ voting records. These results are robust across
5Wright (2007) is a notable exception, who reports null findings. Clark and Williams (2014)
also studies the effect of term limits on legislative behavior but focuses on ideological change
and abstention rates.
6Cain and Levin (1999) and Mooney (2009) provide thorough reviews of this literature.
2
a wide range of model specifications, identification strategies, and characterizations of key
variables. We further show that the effects of term limits were significantly greater in more
professionalized legislatures, where career incentives would be most weakened, and that term
limits significantly increased the share of campaign funding provided by party committees to
legislative candidates. Contrary to the goals of their proponents, terms limits appear to have
exacerbated the legislative consequences of contemporary partisanship and have implications
for understanding how electoral and career incentives affect the quality of governance.
Term Limits and Legislative Behavior
Though American interest in term limits has intensified over the last quarter-century, they
are hardly a novel idea. The ancient Greeks favored a view of representatives as citizen
leaders rather than career politicians and limited many elected officials to a single term in
office. The American Founders also initially supported legislative term limits. During the
Second Continental Congress in 1776, for instance, Thomas Jefferson (1900, 373) warned of
“every danger that might arise to American freedom by continuing too longer in office,”and
the Articles of Confederation prohibited delegates from serving more than three years out
of every six-year period. During the twentieth century, term limits for national legislatures
were enshrined in new constitutions in countries including Costa Rica, Ecuador, and the
Philippines (Carey 1998). The term limits movement in the United States in the last several
decades resulted in their passage in 21 states (and were later repealed or struck down in six).
Existing scholarship investigates a variety of potential effects of term limits on repre-
sentation and legislative behavior. First, term limits may increase the supply of candidates
who traditionally would not seek office. The increased availability of open seat contests may
appeal to potential officeseekers who would be less inclined to challenge an incumbent, and
proponents of term limits have argued that this would facilitate the election of legislators
3
from underrepresented groups, including women and people of color (Glazer and Watten-
berg 1996; Petracca 1992).7 Second, term limits may change how legislators make decisions.
For instance, some have argued that term limits induce a “Burkean shift” (Carey, Niemi,
and Powell 1998; Carey et al. 2006) in which legislators behave more as trustees by voting
for policies they believe are in the long-term interests of their constituents rather than as
delegates who are tethered to constituent opinion and electoral pressures. Third, term lim-
its may affect overall government performance, including legislators’ budgetary effectiveness
(Kousser 2005), state fiscal performance (Keele, Malhotra, and McCubbins 2013), the com-
plexity of policies passed by state legislatures (Kousser 2006), and the legislature’s power
vis-a-vis other institutions (Miller, Nicholson-Crotty, and Nicholson-Crotty 2011).
Despite widespread attentiveness to party polarization and proponents’ emphasis on term
limits’ potential for reducing legislative gridlock and partisan influences in legislatures, few
studies have directly examined this relationship. Using data from roll call votes in state
legislatures in the 1999-2000 sessions, Wright (2007) provides the best empirical evidence
to date on term limits’ relationship with polarization in state legislatures. In comparing
legislative behavior in states with and without term limits, Wright finds no evidence that
term limits are associated with greater polarization at the aggregate level, nor does he
find that that individual legislators compiled more ideologically extreme voting records in
states with term limits. While Wright’s data collection is impressive, the research design
limits strong conclusions about the effect of term limits on polarization. As Wright shows,
polarization varies considerably across states due to factors beyond the implementation of
term limits, and thus a cross-sectional comparison of chambers and legislators cannot tell us
whether polarization increased or decreased in term-limited states compared to what would
have been observed in their absence. Moreover, many of the term-limited states in Wright’s
7The lack of evidence supporting this claim, however, led Carey et al. (2006) to characterize
it as “the dog that won’t bark.”
4
analysis had passed but not yet implemented term limits, thereby limiting what can be
learned about the effects of term limits once legislators had begun to be termed out.
Other research on term limits provides mixed conclusions about their implications for
polarization and representation. In a study of the term-limited California legislature, Cain
and Kousser (2004) found that termed-out legislators vote with their party more frequently,
but downplay term limits as a major source of polarization. Titiunik and Feher (2017) take
advantage of a natural experiment that randomly imposed term limits on legislators in the
Arkansas state senate, and similarly find no evidence that term limits increased ideological
shirking. Cain, Hanley, and Kousser (2006) report that term limits argue that term lim-
its’ impact on legislative polarization may have had more substantial effects had they not
been enacted during an era in which polarization increased across all levels of government.
However, research by Clark and Williams (2014) indicates that legislators who are termed-
out exhibit greater ideological drift and abstain at greater rates. Recent research on term
limits in the Michigan legislature similarly concludes that term limits have “unfastened the
electoral connection” between voters and legislators by increasing the number of lame-duck
legislators (Sarbaugh-Thompson and Thompson 2017, 72-3), but it is unclear whether these
findings apply more systematically across states.
How Term Limits Affect Polarization
We argue that term limits contribute to greater polarization in state legislatures and identify
two key mechanisms that generate these effects. First, at the individual level, term limits
reduce the incentives for legislators to learn about and respond to the interests of their
constituents. The threat of electoral sanction provides incentives for legislators to represent
their constituents while in office (Barro 1973; Ferejohn 1986). Most obviously, therefore,
term limits sever the electoral connection (Mayhew 1974) for officeholders serving in their
5
final legislative terms.8 Even before their final terms, however, legislators seeking to advance
to higher office may perceive that audiences other than their district constituencies hold
greater importance for realizing their career ambitions. The shorter time horizons reduce
the need for legislators to establish personal relationships with voters in their districts and
the relatively weak relationship between legislative behavior and election results (Rogers
2017) suggests that they may suffer few electoral penalties as a consequence.
Instead, legislators in states with term limits may be relatively more free to pursue their
own favored policy goals. They may also perceive incentives to toe the party line more closely,
as future career advancement depends more heavily on support from their party, rather from
the electoral constituency they represent for a relatively limited period of time. Consistent
with this claim, Swift and VanderMolen (2016) use cosponsorship patterns to show that
legislators in term-limited states engage in fewer instances of bipartisan collaboration. Col-
lectively, the reduced electoral incentives for legislators to respond to constituency opinion
and pursue relatively moderate policies increases ideological extremity among legislators and
generates greater polarization across party lines.
Second, we posit that the diminished opportunities for career advancement in term-
limited legislatures dissuade otherwise-qualified potential candidates from seeking office (Hall
Forthcoming). Individuals drawn to public service or who possess policy expertise and are
motivated to craft good public policy may be less inclined to seek election to a state legisla-
tive position whose time horizon is relatively short (Mondak 1995; Montcrief and Thompson
2001; Powell 2000). Not only do term limits induce greater turnover by prohibiting legisla-
tors from seeking office once they have served the allotted number of terms, but term limits
may also encourage state legislators to seek higher office (such as state senate or U.S. House)
8This logic may explain why state economies fare worse under incumbent governors who
are prevented by term limits from seeking another term in office compared to states with
re-election-eligible incumbents (Alt, Bueno de Mesquita, and Rose 2011).
6
earlier than they otherwise would (Francis and Kenny 1997; Ban, Llaudet, and Snyder 2016).
To fill these candidate vacancies, parties expend greater effort to recruit potential candidates,
which leads them to favor candidates with stronger ideological views whose support for the
party program is more assured. Similarly, term limits may empower ideologically-oriented
interest groups and other donors to play a greater role in recruiting and promoting candi-
dates who are reliable allies for their preferred agendas. As Kurtz, Cain, and Niemi (2007)
argue, the greater frequency of open seats may allow parties and interest groups to play a
greater role in the legislative process by recruiting candidates with less experience legislating
and fundraising, and who are therefore more dependent on them (see also Montcrief and
Thompson 2001).
Studies of individual state legislatures provide evidence for how term limits increase the
roles played by parties and other interested political actors. Officials in Maine after that
state’s adoption of term limits reported that “[m]ore members are coming to the legislature
with particular agendas” and that “there is more external [party] focus on recruitment than
in the past” (Powell and Jones 2005, 8). As a consequence, one official expressed the view
that “[t]he legislature has become more ideological with fewer moderates” (Powell and Jones
2005, 8). Masket and Shor (2015, 86) document similar phenomena in a study of the (offi-
cially nonpartisan) Nebraska legislature, where “[t]he forced retirement of a large segment
of the legislature in 2006 due to term limits spurred the parties and the governor into ac-
tion, recruiting, training, and funding candidates at levels not previously seen in modern
Nebraska.” Masket and Shor further observe that, as a consequence of Nebraska’s term
limits, legislative candidates are increasingly selected for their expected loyalty to partisan
agendas. They conclude that because elite campaign contributors are increasingly divided
across party lines, “to the extent that legislators want to keep their donors happy, they will
do so by voting more with their party” (Masket and Shor 2015, 86).
Though the mechanisms described above lead us to expect that term limits increase
7
partisan polarization, we suspect that the effects of term limits vary across institutional con-
texts. Chiefly, term limits are likely to have different effects in more professional legislatures
by altering our posited mechanisms in stronger ways. Service in professional legislatures is
a relatively full-time commitment and legislators in these settings have the most expertise
and experience; therefore, term limits should provide the greatest opportunity for outside
actors to influence newly elected legislators in these settings. Similarly, while legislators’
voting records in states with greater professionalism exhibit more congruence with district
opinion (Maestas 2000), the weakening of career opportunities is likely to reduce legisla-
tors’ incentives to continue doing so. Moreover, due to the full-time nature of professional
legislatures, candidate recruitment is likely to be especially challenging when professional
legislatures adopt term limits as the shock to the value of holding office is especially large.
The candidates that emerge in these settings may be less motivated by career incentives
and instead may be especially eager to advance more ideological policy agendas. Therefore,
consistent with other research which argues that term limits have greater effects on legisla-
tors’ career ambitions (Maestas 2000) and the reallocation of power within legislatures (Hall
2014), we argue that the effects of term limits on polarization are moderated by legislative
professionalism with more professional legislatures exhibiting higher levels of polarization in
response to term limits.
Data and Empirical Strategy
We begin our analysis by studying the effects of legislative term limits on aggregate levels of
polarization between 1993 and 2014. In an ideal scenario, we would randomly assign states
to treatment and control conditions, where states assigned to the treatment condition would
implement term limits, and control states would not. Of course, such a research design is
not possible in the context of the U.S. states. Instead, we use observational data in a panel
8
setting to examine our hypothesis that term limits increased polarization in state legislatures.
Our measures of polarization are based on estimates of legislative ideology developed by
Shor and McCarty (2011). These measures use roll call data for all state legislative chambers
to characterize legislators’ voting behavior and are constructed such that they are compa-
rable across states and time. We use these estimates to construct a state-level measure of
Legislative Polarization that reflects the difference in roll call estimates between the median
Democratic and Republican members of the state legislature.9 The values of this variable
range from approximately zero to three, with higher levels indicating states and chambers
with more polarized legislatures. As Shor and McCarty (2011) show, these measures docu-
ment considerable variation in polarization across states and legislative chambers. Because
our focus is on party polarization, we exclude Nebraska and its unicameral, nonpartisan
legislature from our analysis.10 As we discuss below, the Shor and McCarty (2011) scores
are constant by construction across a state legislator’s career; therefore, our estimates of the
effects of term limits reflect the replacement of legislators who leave the chamber once term
limits are implemented with legislators who are systematically more extreme.11
9We use this aggregate measure to avoid chamber-specific polarization measures that are
very sensitive to sample size, as most state legislatures’ upper chambers have fewer than
fifty members. We discuss the results when separated by chamber below.
10There may be many other potential manifestations of partisan conflict in state legislatures
beyond polarization. One possibility, which we discuss in greater detail in the Conclusion,
is that term limits could affect how district lines are drawn, particularly given the reduced
incentives to protect current incumbents’ seats.
11There is not an obvious solution for identifying the within-legislator effects of term limits.
For instance, DW-NOMINATE and related estimates which provide time-varying measures
of legislator behavior smooth changes in voting patterns over the legislators’ time in office,
complicating efforts to identify discontinuities in voting behavior in response to institutional
9
Our primary independent variable is an indicator, Term limits, for whether term limits
were in effect in a given state-year. Term limits were implemented in 14 states (excluding
Nebraska) during the period under study; they took effect first in California and Maine
(1996) and most recently in Nevada (2010).12 Figure 1 displays the share of states using
term limits (for their lower chambers) over the period of study and indicates the timing of
their adoption. While most states that have implemented term limits did so in the late 1990s
or early 2000s, we have good variation in time of implementation across the period of study.
We use a panel design and leverage within-state changes in the presence of term limits to
identify their effects on polarization. We assume, therefore, that the adoption of term limits
is orthogonal to potential outcomes after we condition on time-invariant state characteristics
changes like term limits.
12One may argue that the date of enactment is a more appropriate indicator of treatment
status since legislators may adjust their behavior in anticipation of the impending change
(Carey, Niemi, and Powell 2000). Though we simply lack sufficient data on legislative vot-
ing records to examine the effects of term limits based on when they were enacted (the vast
majority of which occurred in 1990 or 1992, prior to the starting date of the Shor-McCarty
data), we are comfortable using the implementation date as the indicator of treatment sta-
tus for several reasons. First, our theoretical perspective suggests that legislators that are
termed-out are replaced by legislators who differ from them in systematic ways. Thus, the
effect of term limits on polarization is posited to result from, at least in part, the replace-
ment of termed-out legislators rather than changes in behavior from existing officeholders.
Second, to the extent that changes in polarization were due to the enactment of term lim-
its rather than to their implementation, we are likely to underestimate their effects, thus
making our empirical strategy a more difficult test. Third, research on other consequences
of term limits reports similar results whether the date of enactment or implementation is
used (see, e.g., Keele, Malhotra, and McCubbins 2013).
10
CA ME
AR CO MI
AZ FL MT OH SD
MO
OK
LA
NV
0.0
0.1
0.2
0.3
1995 2000 2005 2010 2015
Year
Sha
re o
f Sta
te L
ower
Hou
ses
with
Ter
m L
imits
Figure 1: Implementation of Term Limits
and, in some models, a battery of time-varying covariates. This assumption of parallel trends
maintains that the “treated” states that adopted term limits reacted to them in the same
way that “control” states would have reacted if they had adopted term limits. Given the
circumstances surrounding the passage of term limits13 and the subsequent plausibility of
accounting for potential confounders through state fixed effects in addition to time-varying
controls, our approach provides credible causal estimates of the effect of term limits on state
legislative polarization.
We employ a multi-period, multi-unit difference-in-difference (DID) design, implemented
with the following linear regression model:
Yit = β0 + β1Term Limitsijt + XijtΩ +Di + Tt + εijt, (1)
where Y is the level of polarization in state i in year t, Term limits indicates whether state
i had term limits in effect in year t, Ω is a vector of coefficients for a matrix of time-varying
state covariates Xit described below, Di is an indicator for each state, Tt is an indicator for
each year, and εit is a random error term, which we cluster on state.
13Most term limits requirements were passed via referendum rather than legislation.
11
Though our analyses begin with a bivariate regression of polarization on term limits along
with state and year indicators, we also estimate models which include a variety of other
state-level covariates that could confound the potential effects attributed to term limits.
First, to account for structural features of state governance, we include indicators for the
presence of Divided government, whether the state has a Democratic governor, Legislative
professionalism (Squire 2017),14 and the difference between the share of seats held by the
majority and minority parties (Party competitiveness). We also account for attributes of
the state population, including Population (logged), Per capita income, and Unemployment
rate. Finally, as McCarty, Poole, and Rosenthal (2006) find that congressional polarization
is closely correlated with both immigration and income inequality at the national level, we
also include annual measures of each state’s Percent foreign-born and Gini coefficients to
account for secular trends that may relate to greater party polarization at the national level
and could be associated with state-level polarization. Summary statistics for all variables
are presented in Table A.1.
Panel Evidence
We begin by estimating our baseline model, which represents Equation (1) but includes
only our indicator for Term limits along with state and year fixed effects. The results are
shown in Table 1. The coefficient estimate for Term limits is positive (0.105) and statistically
significant, providing strong evidence that term limits increased legislative polarization. This
finding is robust to the inclusion of the covariates discussed above. Column (2) presents
14Because this measure is updated intermittently, we assign to each state-year the legislative
professionalism score from the nearest year for which the scores are reported. Below we
also discuss results where we use values of legislative professionalism that pre-date the
beginning of our analysis.
12
results when accounting for the partisan and political environment of state legislatures. In
column (3), we report findings when accounting for characteristics of the state population,
and in model (4) we add measures of state foreign-born population and economic inequality.
Across each model, we find strong and consistent evidence that term limits were associated
with significant and positive increases in legislative polarization. Moreover, despite adding
a variety of control variables, the coefficients for Term limits are relatively consistent in
magnitude across each specification, ranging from 0.083 to 0.105.
The results shown in Table 1 are substantively meaningful in addition to statistically sig-
nificant. We evaluated the substantive magnitudes of our estimates by comparing the results
from Table 1 to the within-state variation in polarization. The average within-state standard
deviation is 0.119; restricting the sample to states without term limits, the comparable value
is 0.097.15 Our reported point estimates therefore constitute a shift of nearly one standard
deviation in within-state polarization levels.
We also find that some of our control variables are significantly associated with polar-
ization. Increases in population were associated with greater polarization, while states with
larger majority parties (perhaps proxying for less party competition) and higher per capita
incomes experienced declines in polarization. The coefficient estimates for Divided govern-
ment, however. are all extremely small in magnitude and none are statistically significant.
We also find no significant differences in polarization based on the partisanship of the gov-
ernor or the partisan composition of the electorate (as measured by state voting patterns in
presidential elections). Interestingly, we do not find support for the claims of McCarty, Poole,
and Rosenthal (2006) in the context of state-level polarization: states’ foreign-born popu-
lation shares and economic inequality are negatively associated with polarization, though
neither is statistically distinguishable from zero.
15The median within-state standard deviation is in both cases slightly lower.
13
Table 1: Fixed Effects OLS Estimates: State Legislative Polarization and Term Limits
Dependent variable:
Legislative Polarization
(1) (2) (3) (4)
Term Limits 0.105∗ 0.096∗ 0.083∗ 0.085∗
(0.044) (0.044) (0.034) (0.033)
Divided Gov. −0.007 −0.001 −0.003(0.013) (0.012) (0.012)
Democratic Governor −0.022 0.000 0.001(0.017) (0.015) (0.015)
Leg. Professionalism 0.145 0.022 0.017(0.168) (0.111) (0.109)
Party Competitiveness −0.005∗ −0.004∗ −0.004∗
(0.001) (0.001) (0.001)
ln(Population) 1.000∗ 1.002∗
(0.353) (0.352)
Per Capita Income −0.013∗ −0.013∗
(0.004) (0.004)
Unemployment Rate −0.036 0.021(0.900) (0.896)
Percent Foreign Born −0.004(0.010)
State Gini Coefficient −0.323(0.264)
State Fixed Effects X X X XYear Fixed Effects X X X XProjected R2 0.049 0.091 0.296 0.3Observations 881 881 881 881
Note: Entries are linear regression coefficients with standard errors clustered on states inparentheses. ∗p<0.05 (two-tailed test).
14
Robustness Checks
The results in Table 1 are robust across a wide range of additional analyses. We estimated
a number of alternative models to highlight the plausibility of our assumptions and the
robustness of our results across model specifications. We discuss these additional analyses
below and present the results in the Supplementary Materials in the interest of space.
First, because our binary indicator for states with term limits ignores qualitative dif-
ferences in term limits enacted across states, we adopt a continuous measure of “term-
limitedness” developed by Sarbaugh-Thompson (2010) and subsequently used in Baker and
Hedge (2013). This measure describes the change in turnover due to term limits relative
to turnover from earlier years. We replace our binary treatment variable with this mea-
sure in the same panel specification as above,16 and report the results in Table B.1. Using
this measure, we continue to find strong evidence that term limits substantially increased
polarization in state legislatures.
Second, we employ two alternative modeling strategies to guard against common con-
cerns about the differences-in-differences framework in a panel setting. First, we account for
potential biases that result from controlling for post-treatment covariates. While incorpo-
rating time-varying covariates into the analysis allows us to control for changes in potential
confounders, this approach has two possible issues: first, covariate values in post-treatment
periods are possibly themselves post-treatment; second, the effect of these covariates is re-
stricted to be constant across the period under study. To address these concerns, we estimate
an alternative model in which we fix each state’s pre-treatment value for each covariate but
allow the effects of each covariate to vary over time. The results of this alternative speci-
fication are shown in Table B.2; the point estimates from this specification are extremely
16Specifically, we mark every state-year without term limits with a zero, and give each state-
year with implemented term limits that state’s term-limitedness score.
15
similar to those in Table 1, though reduced power results in estimates significant only at the
p < 0.10 level in some models.
We also estimate an entirely different panel model which includes one-period lagged
dependent variables to account for unobserved state characteristics, rather than state fixed
effects.17 The results of this model specification are shown in Table B.3. The reported
effects are substantively smaller than those reported in Table 1 yet statistically significant.
Consistent with the interpretation offered by Angrist and Pischke (2008), this result suggests
a statistically significant lower bound on the magnitude of the relationship between term
limits and polarization.
Third, we have estimated our model on a matched sample of states. In this context,
matching serves to reduce the dependence of our results on the assumption that our control
variables enter additively and linearly into our estimating equation (Ho et al. 2007).18 For
each state that adopts term limits at any point in our sample, we match control state(s)
on the covariates from 1993 to 1996, which constitutes our pre-treatment period.19 We
present results from model specifications analogous to Models 1 and 4 from Table 1, using
matched samples based on both one-to-one and two-to-one matching procedures. The results,
presented in Table B.4, are extremely similar to those above. The coefficients on Term
17Angrist and Pischke (2008, 246-247) discuss a useful bracketing property of the lagged
dependent variable model in comparison with the fixed effect model from above: if the
fixed effect model is the true model, then the LDV model results will tend to underestimate
the true effect of term limits; if the LDV model is “true,” the fixed effects estimator for
the effect of term limits will be biased upwards.
18Matching can also be used in panel settings to control for time-varying but unobserved
confounding by matching on pre-treatment outcomes; unfortunately, the relatively few
states with outcome data in the earliest years of our panel make this impractical.
19We implement nearest-neighbor matching using the Matching package in R.
16
Limits are between 0.071 and 0.098 and are significant at the p < 0.05 level in models with
covariates. These results ought to ameliorate concerns about model dependence.
Fourth, we have taken care to note that our results are not driven by any particular state
or year. To do so, we have re-estimated our fullest specification (Model 4 in Table 1) while
systematically dropping states and years. The results are presented in Figures B.1 and B.2
and show that our findings are quite consistent across each of these samples. The coefficient
on Term limits is statistically significant in all models and its magnitude is consistently
between 0.06 and 0.10.
Finally, we have re-estimated our results while measuring polarization using measures
of legislator ideology derived from campaign contributions (Bonica 2016). The results are
presented in Table B.5. Though the results are somewhat noisier than those reported above,
they strongly suggest the same pattern (p < 0.10) shown in our models that use the Shor
and McCarty (2011) scores as the dependent variable. This provides confidence that our
findings are not driven by potential idiosyncrasies in the Shor and McCarty (2011) data but
instead reflect a broader trend where more extreme legislators replaced those removed by
term limits.
Taken together, our robustness checks provide strong and consistent evidence in support
of our baseline estimates of the effect of term limits on state legislative polarization. Because
our outcome measure of polarization is a reflection of state legislators’ policy positions and
roll call voting records, this suggests that we can soundly reject term limit advocates’ claims
that term limits would reduce inter-party conflict; rather, our results suggest that they
markedly increase it. Moreover, in the aggregate, our findings suggest that term limits are
associated with declining levels of collective representation. To the extent most constituents
are relatively more moderate than most elected officials (see, e.g., Bafumi and Herron 2010)
and constituent preferences are relatively stable over short periods of time, the increasing
movement of state legislators toward the ideological poles suggest that greater numbers of
17
legislators vote in ways that are less representative of constituent preferences. Rather than
enhancing democratic representation, as proponents of term limits argued they would, our
evidence suggests that term limits may worsen it.
Additional Analyses and Extensions
Though less centrally connected to our theoretical expectations, we conducted additional
analyses to explore how the effects of term limits varied across institutional features of
legislatures and states and the characteristics of individual legislators.20 First, we studied
whether the effects of term limits varied across state legislatures’ upper and lower cham-
bers. As Cain and Levin (1999) report, term limits may have asymmetric effects across
chambers as legislators first learn the craft of legislating in the lower chamber before being
termed out and pursuing office in the upper chamber. Consistent with this account, we
find stronger evidence for the effects of term limits in states’ lower chambers. Term limits
had a positive, substantively large, and statistically significant effect on polarization in lower
chambers; while we continue to find a positive relationship between term limits and polariza-
tion in states’ upper chambers, the estimates are smaller in magnitude and not statistically
significant at conventional levels. Though the coefficient estimates are themselves not sta-
tistically distinguishable across chambers, our results provide suggestive evidence that the
effects reported above are concentrated disproportionately in state lower chambers.
Second, we studied whether term limits had asymmetric effects across political parties.
This investigation stems from substantial scholarly interest in whether growing party po-
larization is driven primarily by disproportionate movement toward the ideological poles
among Republicans. We reestimate our full model (Column 4 from Table 1) separately us-
ing the Democratic legislative median and the Republican legislative median as our outcome
20Detailed results for these analyses are presented in Supplementary Appendix C.
18
variables. The results indicate that the adoption of legislative term limits led to a statisti-
cally significant shift to the ideological right among Republicans (0.063); among Democrats,
term limits were accompanied by a shift in the liberal direction (−0.023) but the estimate
is not statistically distinguishable from zero. Thus, the estimates suggest that term limits’
effects were about three times greater among Republicans than among Democrats, though
we emphasize that we cannot dispositively rule out the null hypothesis that both parties
contributed equally to increased polarization from term limits.
Finally, we conducted analyses at the individual level to study whether the effects of term
limits varied among legislators with varying degrees of electoral security or who served in
leadership positions.21 Using data on state legislative elections, we distinguished marginal
districts as those in which the incumbent legislator won by 10 percentage points or fewer and
interacted this indicator with Term limits.22 While the results suggest that term limits had
a slightly smaller effect on polarization among representatives from marginal districts than
among their safe-seat counterparts, none of the interactions terms is significant at p < .05.
We also interacted Term limits with an indicator for legislators who served as presiding
officers (including President, President Pro Tempore, Speaker of the House, or Speaker of
the House Tempore), majority leaders, minority leaders, and majority and minority whips.23
21Each observation in these analyses is a legislator i serving in state j in year t using the
same covariates as Column (4) in Table 1 and estimating separate models by party.
22Incumbent vote shares may be an imperfect measure of district competitiveness because
they reflect the incumbent’s “personal vote” as well as the constituency’s political compo-
sition.
23Speakers of the House often do not cast votes, and so their preferences may be measured
with error. However, the inclusion of many leaders who do cast votes should weigh against
any systematic biases. In addition, because the ideology estimates are static, they do
not allow us to account for changes in voting behavior that accompanied the legislators’
19
These data were obtained partially from Fouirnaies and Hall (2015) and Fouirnaies (Forth-
coming) and supplemented with original data collection from the state Yellow Books to
assemble a complete roster of legislative leaders between 1993 and 2014. We find no evi-
dence that term limits had differential effects among leaders and other legislators, as the
coefficient for the interaction between term limits and the indicator for leader is inconsis-
tently signed, substantively small, and not statistically distinguishable from zero. Thus, our
findings do not indicate that legislative leaders, as providers of partisan “brand names” (Cox
and McCubbins 1993), were disproportionately affected by term limits.
Legislative Professionalism, Parties, and Polarization
Our theoretical account posited that the effects of term limits are larger in more profession-
alized legislative settings. By removing experienced legislators who harbor a great deal of
institutional wisdom, term limits empower alternative sources of information and influence
such as parties and ideologically-motivated interest groups. The reduction in the value of
holding office that accompanies term limits also means that the full-time nature of legisla-
tive service limits the potential pool of candidates. Moreover, in these states the removal of
experienced legislators requires parties to engage in more significant efforts to recruit can-
didates for office. Therefore, evidence of larger effects of term limits in more professional
legislatures would also provide suggestive evidence in support of our proposed mechanism
in which term limits increase polarization by shifting legislative power shifting away from
individual legislators and toward political actors with more extreme policy views such as
elevation to leadership positions. However, to the extent legislators’ voting records are
expressions of their personal ideologies and constituency interests, this analysis allows us
to study whether extreme legislators were more likely to serve as leaders in term-limited
states.
20
parties and interest groups.
To test whether professionalism moderates the effect of term limits on polarization, we
interact our term limits indicator with the continuous professionalism measure reported in
Squire (2017). We present the results in a marginal effects plot shown in Figure 2.24 The
x -axis shows values of legislative professionalism and the y-axis plots the estimated effect of
term limits on polarization across these values. The solid line plots these marginal effects
and the dotted lines represent the 95% confidence intervals. The tick marks along the x -axis
indicate the distribution of values of professionalism.25
The results are consistent with our expectations. Among states with low levels of profes-
sionalism, term limits have little if any effect on polarization. However, the effect increases
strongly with professionalism and suggests that the polarizing effect of term limits are con-
centrated in highly professional states. For context, our estimates suggest that the states at
the twentieth percentile of legislative professionalism (the relatively non-professional Georgia
and Mississippi) would experience a relatively small (and statistically insignificant) increase
in polarization of 0.048 after term limits are adopted, while the effects would be more than
twice as large for the state at the eightieth percentile (Maryland), with a statistically and
substantively significant increase of 0.101.
Legislative professionalism is composed of a number of constituent elements, which have
varying degrees of relevance for our theoretical claims. Using data from Bowen and Greene
24The full table of coefficients is shown in Column 1 of Table D.1.
25As the rugplot indicates, the distribution of legislative professionalism is quite right-skewed.
Our results are robust to analysis using the logged value of legislative professionalism as the
moderator (see Table D.2 and Figure D.4). We also conduct the analysis with alternative
measures of professionalism produced by Bowen and Greene (2014). These results are
presented in Figures D.2 and D.3. We find a similarly positive moderating effect using
their first dimension of professionalism, and no relationship with the second dimension.
21
Figure 2: Marginal Effect of Term Limits over Legislative Professionalism.
0.0
0.1
0.2
0.3
0.4
0.0 0.2 0.4 0.6
Legislative Professionalism
Mar
gina
l Effe
ct
The x -axis shows values of legislative professionalism and the y-axis plots the estimated effect of term limitson polarization across these values. The solid line plots these marginal effects and the dotted lines representthe 95% confidence intervals. The horizontal dashed line at zero shows the null hypothesis of no effect ofterm limits. The tick marks along the x -axis indicate the distribution of values of professionalism. The figureshows that the effect of term limits is larger in more professional state legislatures.
(2014), we also explored the degree to which three component elements of professionalism
— legislative salary, session length, and expenditures per legislator — moderate the effect of
term limits. Because session length is a reasonable proxy for the extent to which legislating
is a full-time job, it is most closely associated with the challenges term limits present for
securing legislative candidates in states with more professionalized legislatures. Full-time
legislatures are also the settings where decreases in legislator expertise may provide the
largest openings for parties, interest groups, and other actors to most dramatically affect the
kinds of information legislators bring to bear when making roll call voting decisions. Our
theoretical expectations for per-legislator expenditures and salaries are more ambiguous. We
find that while each of the three component parts are positive moderators, session length in
particular stands out. States with longer sessions see substantially larger polarizing effects of
term limits, while states with higher legislative salaries and higher per-legislator expenditures
22
do not have markedly higher polarizing effects.26 These analyses therefore provide support
both for our expectation that the effects of term limits are moderated by professionalism
and for our proposed mechanisms.
Campaign Finance and Opportunities for Party Influence
Finally, we explore our proposed mechanisms more directly. Our primary theoretical claim
is that term limits generate opportunities and incentives for political parties and similarly
motivated interest groups to further involve themselves in state legislative politics. When
the political knowledge and experience housed in experienced legislators is removed, parties
help fill the vacuum left behind and contribute to greater polarization. While we lack the
research design to conduct a well-identified mediation analysis, we focus on studying whether
term limits are associated with plausible mechanisms that could explain our findings above.
We test the hypothesis that parties demonstrated heightened involvement in state leg-
islative politics after term limits were adopted. Specifically, we investigate whether term
limits increased the share of campaign contributions legislative candidates received from
party campaign organizations.27 The dependent variable was collected from Bonica (2016)
and characterizes the average share, measured in percentage points, of legislative campaign
contributions that are attributable to parties in each legislative election year from 1993 to
26The results of these analyses are presented graphically in Figure D.1 with the full set
of coefficients in Table D.1. As with legislative professionalism, we also perform these
interactions with logged values of these moderators. These results are presented in Table
D.2 and Figures D.5 through D.7.
27Parties could also exert greater influence over voting patterns in term-limited states
through, e.g., increased party leadership control in the legislature (Sarbaugh-Thompson
and Thompson 2017).
23
2014. We expect that term limits increased the share of contributions from political parties
to legislative candidates, particularly in more professionalized states.
Figure 3 shows the results of this additional analysis.28 The figure plots the marginal
effect of term limits on the share of party contributions across the range of values of profes-
sionalism. Among states with the lowest levels of professionalism, our results suggest that
party contributions comprised smaller shares of legislative candidates’ contributions, though
this result is substantively small and not statistically distinguishable from zero. However,
term limits increased parties’ contribution shares in more professional state legislatures.
Among the most professional states, such as California, Michigan, Pennsylvania, and New
York, our results imply that the adoption of term limits is associated with a more than 7.5
percentage point increase in the share of state legislative campaign finance contributions
deriving from party organizations. This finding provides strong evidence that term lim-
its increased the involvement of party organizations in recruiting and supporting legislative
candidates and is consistent with our proposed mechanisms that link this increase in party
involvement to subsequent increases in party polarization.
Term Limits and Electoral Competition
Proponents of term limits argued that they would increase electoral competition and open
up opportunities for legislative service to candidates who may not otherwise seek office.
The available evidence, however, suggests that term limits may not have realized this goal.
Instead, Rogers (2014) reports that while term limits may increase electoral competition
in the first election following the removal of a term-limited incumbent, the incumbency
advantage in subsequent elections is larger than in states without term limits. Though these
findings run contrary to the arguments put forth by term limits’ supporters, they suggest an
28The full table of results is shown in Table D.3.
24
Figure 3: Marginal Effect of Term Limits over Legislative Professionalism, Party Contribu-tion Share Outcome
0.00
0.05
0.10
0.0 0.2 0.4 0.6
Legislative Professionalism
Mar
gina
l Effe
ct
The x -axis shows values of legislative professionalism and the y-axis plots the estimated effect of term limitson the share of campaign funds contributed by party organizations across these values. The solid line plotsthese marginal effects and the dotted lines represent the 95% confidence intervals. The horizontal dashedline at zero shows the null hypothesis of no effect of term limits. The tick marks along the x -axis indicatethe distribution of values of professionalism. The estimates indicate that term limits significantly increasedthe share of legislative candidates’ contributions from political parties in states with more professionallegislatures.
alternative explanation for the findings shown above: that increases in polarization are due
to declining electoral competition in term-limited states.
To explore this alternative explanation, we calculated the share of competitive legislative
elections in each state. We identified competitive elections as those where the margin of
victory was five percentage points or fewer, or ten percentage points or fewer.29 We then
estimated our full model specification (column 4 of Table 1) but used these indicators of
29These data were obtained from ICPSR study 34297 and supplemented with: Klarner, Carl,
2013, “State Legislative Election Returns Data, 2011-2012,” hdl:1902.1/21549. Due to
missing elections results for some states and years, we included only those states where
elections results for at least half of the seats were available.
25
competitiveness as the dependent variables. If declines in electoral competition due to term
limits explain our results, these declines must have been especially large in states with more
professional legislatures. Therefore, we also estimate models that include the interaction
between term limits and professionalism.
In short, we find no evidence that our main findings are driven by declines in electoral
competition rather than by increased party involvement.30 Overall, term limits are asso-
ciated with declines in electoral competition. Across both indicators of competition, the
proportion of legislative seats decided by fewer than five percentage points was four to six
percentage points lower in states with term limits. However, the interaction terms for legisla-
tive professionalism are positive, indicating that term limits had smaller effects on electoral
competition in states with more professional legislatures. This latter finding, in particu-
lar, contrasts with the pattern of results shown above. Though not dispositive, the results
strongly weigh against the possibility that changes in the larger electoral environment that
accompanied term limits explain our main results. Moreover, they further weigh against
claims proffered by term limits’ supporters. Not only do term limits increase party conflict
and polarization, but they also may lead to further decreases in electoral competition.
Conclusion
Scholars, political observers, and the public consistently identify partisan conflict, ideolog-
ical polarization, and the accompanying legislative gridlock as major sources of frustration
in contemporary American politics. For the last quarter century, proponents of term limits
have argued that limiting the number of years legislators can serve in office would remedy
polarization and other perceived legislative ills. Our analysis, however, provides no evidence
that term limits ameliorate partisan conflict in state legislatures; instead, we find strong and
30See Table D.4.
26
consistent evidence that term limits increase partisan polarization. To the extent most citi-
zens have relatively moderate ideologies, our findings suggest that term limits have amplified
“leapfrog representation” (Bafumi and Herron 2010) and reduced the quality of collective
representation.
Our findings contribute new evidence about the relationship between the electoral con-
nection and democratic governance. Elections are the primary way through which citizens
affect government activity in democratic societies and electoral incentives are a key mecha-
nism for ensuring that government actors behave in ways that reflect citizen preferences. As
Madison wrote in Federalist #51, for instance, “[D]ependence on the people is, no doubt,
the primary control on the government.” By reducing or eliminating legislators’ electoral
incentives, our results suggest that legislators exhibit voting behavior that is less congruent
with the state electorate. Building upon other studies on electoral accountability (Rogers
2017), future research could explore whether legislators in term-limited states receive fewer
sanctions from voters for their roll call behavior and examine whether individual legislators’
behavior deviates more from constituent preferences in their final terms in office. In addition,
while we provided suggestive evidence for parties’ roles in producing greater polarization in
term-limited states, additional research could study other potential actors and mechanisms
that may also contribute to these patterns, such as interest groups and activists. Moreover,
term limits could strengthen parties’ influences within the chamber, perhaps by increas-
ing legislators’ willingness to delegate power to party leaders (see Sarbaugh-Thompson and
Thompson 2017).
By necessity, our findings have important limitations of their own. The estimates of the
effect of term limits on legislative behavior are limited by the data currently available for
characterizing legislative behavior. By assumption, the scores provided by Shor and McCarty
(2011) are constant over time within legislators. Thus, the results uncovered in our research
are driven entirely by changes in legislative composition but do not reflect the possibility
27
that an individual legislator’s behavior may change over time. Our theoretical perspective
suggests that this likely results in an underestimate of term limits’ effects, as legislators may
be increasingly beholden to parties over time as their subsequent electoral careers depend
upon party support. However, further research is needed to identify whether and how term
limits affect within-legislator changes in voting patterns.
Our results suggest that term limits exacerbate partisan conflict as measured by polar-
ization in roll call voting patterns across party lines. But there may be other ways that term
limits generate more partisan outcomes. One of the most important tasks for most state
legislatures, for instance, is to draw legislative districts. Preliminary analyses we conducted
suggested that states with term limits have more partisan districting plans, both for state
legislatures and the U.S. House. Using data on the efficiency gap for states with state leg-
islative elections in 2016, for instance, we found that the efficiency gap was more than 50
percent larger in states with term limits (5.9%) than without (3.6%). This difference is sta-
tistically significant (p < .02) and indicates that legislatures in term-limited states adopted
districting plans that led to significantly more wasted votes. What is more, according to
the standard advocated by Stephanopoulos and McGhee (2015), the efficiency gap was large
enough (more than 8 percentage points) to trigger presumptive constitutional review in 31%
of term-limited states, compared with only 3% of states without term limits. We find similar
patterns based on U.S. House districting, where the efficiency gap is generally larger and
provides greater partisan advantages in states with term limits. We emphasize that these
results are preliminary and not dispositive, nor do they permit causal inferences. However,
they are consistent with the manifestation of heightened partisanship that accompanies the
implementation of term limits. Additional research could study other legislative outcomes
that may have been affected by term limits.
Finally, while our results focus on how term limits affect one observable component of
legislative behavior, partisan polarization in roll call voting records, our analysis does not
28
directly address how term limits may affect other dimensions of legislative behavior that
may have implications for political representation. For instance, term limits could induce a
“Burkean” shift in representation such that legislators act more as trustees rather than as
delegates. Our estimates of legislative ideology based on roll call voting scores do not allow
us to evaluate whether term limits change the way legislators make decisions. They also
do not characterize whether term limits affect the policies considered or adopted by state
legislatures. In addition, legislators can also represent their constituents through securing
distributive outlays and providing constituency service. Future research is necessary to study
how term limits affect legislative behavior on these additional dimensions.
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Supplementary Materials for
“Legislative Term Limits and Polarization”
Contents
A Summary Statistics 37
B Robustness Checks, Polarization Result 38
B.1 Term-Limitedness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
B.2 Pre-Treatment Covariates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
B.3 LDV Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
B.4 Matched Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
B.5 Sample Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
B.6 DIME Ideal Point Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
C Additional Results 44
C.1 Chamber-Specific Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
C.2 Party-Specific Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
C.3 Electoral Marginality and Legislative Leadership Status . . . . . . . . . . . . 46
D Robustness Checks, Moderators, and Mechanisms 47
D.1 Professionalism Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
D.2 Professionalism Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
D.3 Alternative Professionalism Measures . . . . . . . . . . . . . . . . . . . . . . 50
D.4 Logged Professionalism Moderators . . . . . . . . . . . . . . . . . . . . . . . 51
D.5 Party Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
D.6 Term Limits and Competition . . . . . . . . . . . . . . . . . . . . . . . . . . 54
36
A Summary Statistics
Table A.1: Summary Statistics of Key Variables
Statistic Mean Median Min Max St. Dev.
Legislative Polarization 1.407 1.372 0.105 3.094 0.483Term Limits 0.192 0 0 1 0.394Divided Gov. 0.505 1 0 1 0.500Democratic Governor 0.427 0.000 0.000 1.000 0.491Leg. Professionalism 0.195 0.167 0.027 0.629 0.122Party Competitiveness 13.218 10.714 0.500 41.137 8.897ln(Population) 15.128 15.274 13.098 17.474 1.024Per Capita Income 33.976 33.246 16.986 66.716 8.507GOP Presidential State Share 0.502 0.498 0.270 0.746 0.091Unemployment Rate 0.055 0.052 0.022 0.135 0.019Percent Foreign Born 7.640 5.416 1.016 29.300 5.845State Gini Coefficient 0.592 0.587 0.521 0.712 0.037
37
B Robustness Checks, Polarization Result
B.1 Term-Limitedness
Table B.1: Robustness: State Legislative Polarization and “Term Limitedness”
Dependent variable:
Legislative Polarization
(1) (2) (3) (4)
Term Limitedness 0.098∗ 0.089 0.087∗ 0.087∗
(0.048) (0.051) (0.043) (0.042)
Divided Gov. −0.006 −0.001 −0.002(0.014) (0.013) (0.013)
Democratic Governor −0.024 −0.001 −0.001(0.017) (0.015) (0.015)
Leg. Professionalism 0.161 0.033 0.029(0.181) (0.120) (0.118)
Party Competitiveness −0.005∗ −0.004∗ −0.004∗
(0.001) (0.001) (0.001)
ln(Population) 1.049∗ 1.053∗
(0.349) (0.347)
Per Capita Income −0.012∗ −0.012∗
(0.004) (0.004)
Unemployment Rate −0.028 0.019(0.884) (0.883)
Percent Foreign Born −0.007(0.011)
State Gini Coefficient −0.269(0.261)
State Fixed Effects X X X XYear Fixed Effects X X X XProjected R2 0.036 0.08 0.294 0.297Observations 881 881 881 881
Note: Entries are linear regression coefficients with standard errors clusteredon states in parentheses. ∗p<0.05 (two-tailed test).
38
B.2 Pre-Treatment Covariates
Table B.2: Fixed Effects OLS Estimates: State Legislative Polarization and Term Limits
Dependent variable:
Legislative Polarization
(1) (2) (3)
Term Limits 0.098∗ 0.086 0.085(0.046) (0.049) (0.048)
Covariates Institutional + Demographic + MPRState Fixed Effects X X XYear Fixed Effects X X XObservations 881 881 881
Note: Entries are linear regression coefficients with standard errors clustered onstates in parentheses. ∗p<0.05 (two-tailed test).
39
B.3 LDV Models
Table B.3: Robustness: Lagged Dependent Variable Model
Dependent variable:
Legislative Polarization
(1) (2) (3) (4)
Term Limits 0.013∗ 0.015∗ 0.014∗ 0.014∗
(0.007) (0.007) (0.007) (0.007)
Divided Gov. 0.001 0.003 0.002(0.003) (0.003) (0.003)
Democratic Governor 0.005 0.007 0.007∗
(0.004) (0.004) (0.004)
Leg. Professionalism −0.022 −0.033 −0.040∗
(0.014) (0.018) (0.019)
Party Competitiveness −0.000 0.000 0.000(0.000) (0.000) (0.000)
ln(Population) 0.004 0.003(0.002) (0.002)
Per Capita Income −0.001∗ −0.001∗
(0.000) (0.000)
Unemployment Rate 0.048 −0.020(0.147) (0.155)
Percent Foreign Born 0.001(0.000)
State Gini Coefficient −0.027(0.067)
Lagged Polarization 1.002∗ 1.004∗ 1.003∗ 1.002∗
(0.003) (0.004) (0.004) (0.005)
Year Fixed Effects X X X XObservations 822 822 822 822
Note: Entries are linear regression coefficients with standard errors clusteredon states in parentheses. ∗p<0.05 (two-tailed test).
40
B.4 Matched Sample
Table B.4: Matched Sample Fixed Effects Model: State Legislative Polarization and TermLimits
Dependent variable:
Legislative Polarization
(1) (2) (3) (4)
Term Limits 0.071 0.074∗ 0.098∗ 0.087∗
(0.041) (0.031) (0.043) (0.032)
Divided Gov. −0.006 −0.003(0.021) (0.017)
Democratic Governor 0.008 0.001(0.026) (0.022)
Leg. Professionalism 0.304 0.159(0.320) (0.210)
Party Competitiveness −0.004 −0.004∗
(0.002) (0.002)
ln(Population) 0.949 1.032∗
(0.503) (0.434)
Per Capita Income −0.010 −0.013∗
(0.007) (0.006)
Unemployment Rate 0.482 −0.349(1.469) (1.227)
Percent Foreign Born −0.014 −0.014(0.017) (0.014)
State Gini Coefficient −0.262 −0.259(0.350) (0.315)
Matching One-to-One One-to-One Two-to-One Two-to-OneState Fixed Effects X X X XYear Fixed Effects X X X XProjected R2 0.029 0.217 0.051 0.277Observations 451 451 577 577
Note: Entries are linear regression coefficients with standard errors clustered on states inparentheses. Sample is matched using inverse propensity weighting on the basis of pre-treatment (1993-1996) covariates using the Matching package in R. ∗p<0.05 (two-tailedtest).
41
B.5 Sample Robustness
0.00
0.05
0.10
0.15
AK AL AR AZ CACOCT DE FL GA HI IA ID IL IN KS KY LA MAMDME MI MNMOMSMTNCNDNH NJNMNVNYOHOKOR PA RI SCSD TN TX UT VA VTWAWIWVWY
Dropped State
Coe
ffici
ent o
n 'T
erm
Lim
its'
Figure B.1: Coefficients when Successively Dropping States
0.00
0.05
0.10
0.15
1995 2000 2005 2010 2015
Dropped Year
Coe
ffici
ent o
n 'T
erm
Lim
its'
Figure B.2: Coefficients when Successively Dropping Years
42
B.6 DIME Ideal Point Estimates
Table B.5: Polarization and Term Limits, Campaign Finance Polarization Measure
Dependent variable:
Legislative Polarization (DIME Outcome)
(1) (2) (3) (4)
Term Limits 0.081 0.085 0.077 0.095(0.057) (0.058) (0.056) (0.057)
Divided Gov. −0.022 −0.027 −0.032(0.028) (0.029) (0.029)
Democratic Governor 0.000 0.001 0.003(0.026) (0.025) (0.023)
Leg. Professionalism −0.279 −0.239 −0.226(0.659) (0.694) (0.691)
Party Competitiveness −0.001 −0.002 −0.002(0.004) (0.003) (0.004)
ln(Population) 0.072 0.046(0.319) (0.303)
Per Capita Income 0.011 0.008(0.013) (0.014)
Unemployment Rate 2.879 2.941(2.091) (2.129)
Percent Foreign Born −0.008(0.032)
State Gini Coefficient −1.299(0.799)
State Fixed Effects X X X XYear Fixed Effects X X X XProjected R2 0.008 0.015 0.036 0.055Observations 382 382 382 382
Note: Entries are linear regression coefficients with standard errors clustered onstates in parentheses. ∗p<0.05 (two-tailed test).
43
C Additional Results
C.1 Chamber-Specific Estimates
Table C.1: Fixed Effects OLS Estimates: State Legislative Polarization and Term Limits
Dependent variable:
Lower Chamber Polarization Upper Chamber Polarization
(1) (2)
Term Limits 0.108∗ 0.014(0.034) (0.080)
Divided Gov. −0.001 0.036(0.015) (0.019)
Democratic Governor 0.011 −0.009(0.017) (0.028)
Leg. Professionalism 0.063 0.072(0.098) (0.237)
Party Competitiveness −0.003∗ 0.000(0.001) (0.003)
ln(Population) 0.843∗ 1.023∗
(0.339) (0.490)
Per Capita Income −0.016∗ −0.016∗
(0.005) (0.007)
Unemployment Rate 0.513 −3.725(0.901) (2.442)
Percent Foreign Born −0.001 −0.001(0.011) (0.019)
State Gini Coefficient −0.346 0.662(0.277) (0.603)
State Fixed Effects X XYear Fixed Effects X XProjected R2 0.282 0.125Observations 881 881
Note: Entries are linear regression coefficients with standard errors clustered on states in parentheses.∗p<0.05 (two-tailed test).
44
C.2 Party-Specific Estimates
Table C.2: Fixed Effects OLS Estimates: Republican Median Legislator and Term Limits
Dependent variable:
Democratic Median Republican Median
(1) (2)
Term Limits −0.023 0.063∗
(0.030) (0.025)
Divided Gov. −0.000 −0.003(0.011) (0.007)
Democratic Governor 0.016 0.017(0.013) (0.010)
Leg. Professionalism 0.111 0.127(0.096) (0.075)
Party Competitiveness 0.001 −0.003(0.002) (0.002)
ln(Population) −0.717∗ 0.285(0.272) (0.185)
Per Capita Income 0.008∗ −0.005(0.004) (0.003)
Unemployment Rate 0.784 0.805(0.639) (0.548)
Percent Foreign Born 0.008 0.004(0.008) (0.005)
State Gini Coefficient 0.297 −0.026(0.215) (0.153)
State Fixed Effects X XYear Fixed Effects X XProjected R2 0.199 0.207Observations 881 881
Note: Entries are linear regression coefficients with standard errors clusteredon states in parentheses. ∗p<0.05 (two-tailed test).
45
C.3 Electoral Marginality and Legislative Leadership Status
Table C.3: Effect of Term Limits Across District Marginality and Leadership Status
Dependent variable:
S-M Score, Dems S-M Score, Reps S-M Score, Dems S-M Score, Reps |S-M Score|
(1) (2) (3) (4) (5) (6)
Term Limits −0.040 0.077∗ −0.022 0.031 0.091∗ 0.054∗
(0.028) (0.026) (0.027) (0.022) (0.025) (0.023)
Marginal District 0.084∗ −0.006 −0.064∗
(0.020) (0.008) (0.017)
Leader −0.065∗ 0.031∗ 0.026∗
(0.014) (0.011) (0.011)
Term Limit × Marginal District 0.005 −0.042 −0.014(0.046) (0.024) (0.033)
Term Limit × Leader 0.006 −0.016 0.009(0.029) (0.015) (0.022)
State Fixed Effects X X X X X XYear Fixed Effects X X X X X XCovariates X X X X X XProjected R2 0.018 0.006 0.009 0.005 0.01 0.004Observations 15,255 15,010 68,547 65,484 30,265 134,031
Note: Entries are linear regression coefficients with standard errors clustered on states in parentheses. ∗p<0.05 (two-tailedtest).
46
D Robustness Checks, Moderators, and Mechanisms
D.1 Professionalism Figures
Figure D.1: Effect of Term Limits over Legislative Professionalism Component Parts
0.0
0.1
0.2
0.3
0 100 200
Legislative Salary
Mar
gina
l Effe
ct
(a) Legislative Salary
0.0
0.1
0.2
0.3
0.4
100 200 300 400 500
Session Length
Mar
gina
l Effe
ct
(b) Session Length
−0.1
0.0
0.1
0.2
0 2000 4000
Expenditures per Legislator
Mar
gina
l Effe
ct
(c) Per-Legislator Expenditures
The x -axes show values of each component of legislative professionalism and the y-axes plot the estimatedeffect of term limits on polarization across these values. The solid lines plot these marginal effects andthe dotted lines represent the 95% confidence intervals. The horizontal dashed lines at zero show the nullhypothesis of no effect of term limits. The tick marks along the x -axis indicate the distribution of valuesof professionalism. Though each slope has a positive estimate, we find the strongest evidence that sessionlength moderates the effect of term limits on polarization.
47
D.2 Professionalism Tables
Table D.1: Fixed Effects Model: State Legislative Polarization, Term Limits, and Profes-sionalism
Dependent variable:
Legislative Polarization
(1) (2) (3) (4)
Term Limits 0.015 0.015 0.056 0.084(0.052) (0.043) (0.044) (0.046)
Leg. Professionalism −0.018(0.112)
Session Length −0.000(0.000)
Leg. Salary −0.001(0.001)
Exp. Per Legislator 0.000(0.000)
Divided Gov. −0.002 −0.004 −0.001 −0.002(0.013) (0.013) (0.012) (0.012)
Democratic Governor −0.002 −0.004 0.000 0.002(0.015) (0.015) (0.015) (0.015)
Party Competitiveness −0.005∗ −0.005∗ −0.004∗ −0.004∗
(0.001) (0.001) (0.001) (0.001)
ln(Population) 1.016∗ 1.015∗ 1.041∗ 0.967∗
(0.348) (0.345) (0.351) (0.357)
Per Capita Income −0.012∗ −0.012∗ −0.013∗ −0.013∗
(0.004) (0.004) (0.004) (0.004)
Unemployment Rate −0.003 −0.031 −0.070 0.084(0.907) (0.894) (0.882) (0.913)
Percent Foreign Born −0.005 −0.005 −0.004 −0.005(0.010) (0.011) (0.010) (0.010)
State Gini Coefficient −0.292 −0.275 −0.397 −0.294(0.258) (0.263) (0.247) (0.258)
Leg. Professionalism × Term Limits 0.310(0.206)
Session Length × Term Limits 0.000∗
(0.000)
Leg. Salary × Term Limits 0.001(0.000)
Exp. Per Legislator × Term Limits 0.000(0.000)
State Fixed Effects X X X XYear Fixed Effects X X X XProjected R2 0.31 0.309 0.308 0.306Observations 881 881 881 881
Note: Entries are linear regression coefficients with standard errors clusteredon states in parentheses. ∗p<0.05 (two-tailed test).
48
Table D.2: Fixed Effects Model: State Legislative Polarization, Term Limits, and LoggedProfessionalism
Dependent variable:
Legislative Polarization
(1) (2) (3) (4)
Term Limits 0.235∗ −0.390 −0.010 0.044(0.084) (0.215) (0.136) (0.220)
ln(Leg. Professionalism) 0.002(0.028)
ln(Session Length) −0.008(0.019)
ln(Leg. Salary) −0.048∗
(0.023)
ln(Exp. Per Legislator) 0.019(0.057)
Divided Gov. −0.003 −0.005 −0.000 −0.002(0.013) (0.013) (0.012) (0.012)
Democratic Governor −0.003 −0.004 0.002 0.002(0.015) (0.015) (0.015) (0.015)
Party Competitiveness −0.005∗ −0.005∗ −0.004∗ −0.004∗
(0.001) (0.001) (0.001) (0.001)
ln(Population) 1.003∗ 1.011∗ 1.032∗ 0.991∗
(0.344) (0.338) (0.349) (0.357)
Per Capita Income −0.012∗ −0.012∗ −0.012∗ −0.013∗
(0.004) (0.004) (0.004) (0.004)
Unemployment Rate −0.080 −0.040 0.021 0.017(0.898) (0.887) (0.902) (0.901)
Percent Foreign Born −0.006 −0.006 −0.006 −0.004(0.011) (0.011) (0.010) (0.010)
State Gini Coefficient −0.263 −0.256 −0.351 −0.333(0.255) (0.263) (0.260) (0.268)
ln(Leg. Professionalism) × Term Limits 0.091∗
(0.042)
ln(Session Length) × Term Limits 0.098∗
(0.045)
ln(Leg. Salary) × Term Limits 0.025(0.034)
ln(Exp. Per Legislator) × Term Limits 0.007(0.033)
State Fixed Effects X X X XYear Fixed Effects X X X XProjected R2 0.31 0.309 0.308 0.306Observations 881 881 881 881
Note: Entries are linear regression coefficients with standard errors clusteredon states in parentheses. ∗p<0.05 (two-tailed test).
49
D.3 Alternative Professionalism Measures
0.0
0.1
0.2
0.3
0 3 6 9
Legislative Professionalism (Bowen)
Mar
gina
l Effe
ct
Figure D.2: Effect of Term Limits over Leg. Professionalism (Bowen-Greene First Dim.)
−0.1
0.0
0.1
0.2
0.3
0.4
−2 0 2
Legislative Professionalism (Bowen Second Dimension)
Mar
gina
l Effe
ct
Figure D.3: Effect of Term Limits over Leg. Professionalism (Bowen-Greene Second Dim.)
50
D.4 Logged Professionalism Moderators
−0.2
0.0
0.2
−3 −2 −1
Logged Legislative Professionalism
Mar
gina
l Effe
ct
Figure D.4: Effect of Term Limits over Logged Leg. Professionalism
−0.3
−0.2
−0.1
0.0
0.1
0.2
0 2 4
Logged Legislative Salary
Mar
gina
l Effe
ct
Figure D.5: Effect of Term Limits over Logged Salary
51
−0.1
0.0
0.1
0.2
0.3
3.5 4.0 4.5 5.0 5.5 6.0
Logged Session Length
Mar
gina
l Effe
ct
Figure D.6: Effect of Term Limits over Logged Session Length
−0.1
0.0
0.1
0.2
4 5 6 7 8
Logged Expenditures per Legislator
Mar
gina
l Effe
ct
Figure D.7: Effect of Term Limits over Logged Per-Legislator Expenditures
52
D.5 Party Contributions
Table D.3: Party Campaign Contributions and Term Limits
Dependent variable:
Party Committee Share of Total Contributions
(1) (2) (3) (4)
Term Limits 0.027 0.020 −0.012 −0.006(0.015) (0.011) (0.024) (0.020)
Divided Gov. 0.002 0.002(0.007) (0.007)
Democratic Governor −0.013∗ −0.013∗
(0.005) (0.005)
Leg. Professionalism 0.331∗ 0.298 0.316(0.163) (0.175) (0.165)
Party Competitiveness −0.001 −0.001(0.001) (0.000)
ln(Population) −0.219∗ −0.211∗
(0.096) (0.096)
Per Capita Income −0.005∗ −0.005∗
(0.003) (0.003)
Unemployment Rate 0.032 0.021(0.357) (0.354)
Percent Foreign Born −0.008 −0.008(0.009) (0.009)
State Gini Coefficient −0.048 −0.036(0.138) (0.138)
Term Limits × Leg. Professionalism 0.178 0.120(0.092) (0.074)
State Fixed Effects X X X XYear Fixed Effects X X X XProjected R2 0.011 0.135 0.06 0.137Observations 394 394 394 394
Note: Entries are linear regression coefficients with standard errors clustered on states inparentheses. ∗p<0.05 (two-tailed test).
53
D.6 Term Limits and Competition
Table D.4: Fixed Effects OLS Estimates: Electoral Competition and Term Limits
Dependent variable:
Share Races Close (5%) Share Races Close (10%)
(1) (2) (3) (4)
Term Limits −0.053∗ −0.086∗ −0.044 −0.081(0.024) (0.035) (0.024) (0.041)
Divided Gov. 0.020∗ 0.021∗ 0.014 0.015(0.009) (0.009) (0.008) (0.008)
Democratic Governor 0.014 0.012 0.013 0.011(0.008) (0.009) (0.009) (0.009)
Leg. Professionalism −0.114 −0.140 −0.057 −0.087(0.150) (0.156) (0.126) (0.131)
Party Competitiveness 0.002 0.002 0.001 0.001(0.001) (0.001) (0.001) (0.001)
ln(Population) −0.334∗ −0.329∗ −0.458∗ −0.452∗
(0.148) (0.146) (0.130) (0.126)
Per Capita Income 0.001 0.002 −0.000 0.000(0.003) (0.003) (0.003) (0.003)
Unemployment Rate −0.036 −0.007 −0.068 −0.034(0.704) (0.707) (0.650) (0.650)
Percent Foreign Born −0.000 −0.001 −0.006 −0.007(0.009) (0.009) (0.008) (0.008)
State Gini Coefficient 0.189 0.210 0.211 0.235(0.363) (0.365) (0.272) (0.272)
Term Limits × Leg. Professionalism 0.136 0.156(0.101) (0.126)
State Fixed Effects X X X XYear Fixed Effects X X X XProjected R2 0.08 0.084 0.084 0.089Observations 360 360 360 360
Note: Entries are linear regression coefficients with standard errors clustered on states inparentheses. ∗p<0.05 (two-tailed test).
54
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